FH

FHIR Auth – An Authorization Server for FHIR Resource Servers

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FHIR Auth – An Authorization Server for FHIR Resource Servers

Hello, merry Christmas and happy holidays. I've been working on FHIR Auth for the past couple of months. FHIR auth is an authorization server that follows the SMART of FHIR authorization standard published in FHIR documentation, and compatible with most FHIR resource servers. FHIR Auth can grand resource level create, read, update, delete and search privileges. The project is still work in progress and much improvements are needed, but it now possible to perform the authorization requirements expected of a FHIR authorization server. For maintainability the frontend and server is developed in two separate repositories. I'd love to get contribution from the community. Happy 2023 everyone. FHIR Auth server - https://github.com/zemantic/fhir-auth FHIR Auth frontend - https://github.com/zemantic/fhir-auth-frontend

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Indie HackersFits the IH revenue-focused audience · Strong signals: para, compatible · Missing: supports, reddit linkedin, podcasting
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month, para · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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